Computing and Information Systems - Theses
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ItemWorkflow Scheduling in Cloud and Edge Computing Environments with Deep Reinforcement Learning( 2023-08)Cloud computing has firmly established itself as a mandatory platform for delivering computing services over the internet in an efficient manner. More recently, novel computing paradigms such as edge computing have also emerged to complement the traditional cloud computing paradigm. Owing to the multitude of benefits offered by cloud and edge computing environments, these platforms are increasingly used for the execution of workflows. The problem of scheduling workflows in a distributed system is NP-Hard in the general case. Scheduling workflows across highly dynamic cloud and edge computing environments is even more complex due to inherent challenges associated with these environments including the need to satisfy diverse contradictory objectives, coordinating executions across highly distributed infrastructures and dynamicity of the operating conditions. These requirements collectively give rise to the need for adaptive workflow scheduling algorithms that are capable of satisfying diverse optimization goals amid highly dynamic conditions. Deep Reinforcement Learning (DRL) has emerged as a promising paradigm for dealing with highly dynamic and complex problems due to the ability of DRL agents to learn to operate in stochastic environments. Despite the benefits of DRL, there are multiple challenges associated with the application of DRL techniques including multi-objectivity, curse of dimensionality, partial observability and multi-agent coordination. In this thesis, we propose novel DRL algorithms and architectures to efficiently overcome these challenges.